Associative relevancy knowledge profiling architecture, system, method, and computer program product

ABSTRACT

Provided are architectures, system, methods, and computer program products that provide a user with the ability to define an association of data and/or information from known reference sets perceived by the user as relevant to a subject matter domain, thereby imparting and formalizing some of the user&#39;s knowledge about the domain. An associative relevancy knowledge profiler may also allow a user to create a profile by modifying or restricting the known reference sets and windowing the results from the association as a user might refine any other analysis algorithms. An associative relevancy knowledge profiler may also be used to define a user profile used by the user and others. A user profile may be usable in various manners depending upon, for example, rights management permissions and restrictions for a user.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of International Application No.PCT/US/2008/060984, filed Apr. 21, 2008, and U.S. Provisional PatentApplication No. 60/913,929, filed Apr. 25, 2007, both of which areentitled, “ASSOCIATIVE RELEVANCY KNOWLEDGE PROFILING ARCHITECTURE,SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT.”

FIELD OF THE INVENTION

Embodiments of the present invention relate generally to informationretrieval systems and, more particularly, to knowledge profilingarchitectures permitting capture and use of associations between knownreference sets, and related systems, methods, and computer programproducts.

BACKGROUND

Many different types of data and information are available such as text,articles, lists, and graphics, and many different systems are availablefor recording and classifying data and information in various structure,such as a common database structure or a spreadsheet, all collectivelyreferred to herein as data and information sources regardless of thetype, system, or structure. And many different types of systems havebeen developed for extracting specific information from various data andinformation sources, collectively referred to herein as search engines,including Google® search of Google Corporation of Mountain View, Calif.,Baidu search of Baidu.com Corporation of Beijing, China, Yahoo!® andAltaVista® searches of Yahoo! Corporation of Sunnyvale, Calif., MSN® andWindows Live® searches of Microsoft Corporation of Redmond, Wash., andlike general searching applications; SQL and like customized searchingapplications; and other information retrieval (IR) systems. And evensome systems have been developed for trying to infer and/or calculateinformation from one or more data and/or information sources, such asidentifying trends in data and hypothesis generation, includinginference systems, deductive reasoning systems, artificial intelligencesystems, neural network systems, semantic network systems, fuzzy logicsystems, and other expert systems, collectively referred to herein asinference engines.

Further, some systems allow a user to save preferences and establishpreset conditions that can later be used again and/or refined fordifferent purposes, such as a default search strategy that can berefined for various more specific searches area. Some systems aredesigned for particular types of data and information. And some systemsare designed for a particular subject matter domain, and thecorresponding operations that might be performed on the data andinformation available for the particular subject matter domain.

But although techniques have been developed for working with data andinformation, including many sophisticated search engines and inferenceengines, it is desirable to improve upon these existing techniques andto provide the further ability for a user to impart his or her knowledgeabout a domain, including to impart knowledge about a domain separateand apart from a particular search engine or inference engine. Whilesearch engines and inference engine provide exceptionally important anduseful advantages, these systems, individually and in combination, areprincipally limited to working with, managing, and creating data andinformation. For example, there is a need in the art for improvedarchitectures, systems, methods, and computer program products forproviding a user with the ability to define and modify an association ofdata and/or information that is perceived by the user as relevant to asubject matter domain, thereby imparting some of his or her knowledgeabout the domain, and permitting use of that knowledge to performdiscovery process operations on data and information, i.e., to evaluatedata and information.

SUMMARY

In light of the foregoing background, embodiments of the presentinvention provide associative relevancy knowledge profilingarchitectures, systems, methods, and computer program products forcapturing relevant associations between known reference sets as a way ofcapturing knowledge from a domain expert, also referred to as a subjectmatter expert (SME). Embodiments of the present invention are notintended to replace search engines or inference engines, but areintended to provide a separate type of technology that may be used, forexample, to improve upon search engines and inference engines. Notably,embodiments of the present invention should not be confused with asearch engine or inference engine, a search profile, a navigationhistory, or a search history. Embodiments of the present invention are aseparate technology designed to provide additional capabilities andfunctionalities that are not known to exist, apart from or inconjunction with use in the context of a search engine or inferenceengine.

Embodiments of the present invention provide a user with the ability todefine an association of data and/or information that is perceived bythe user as relevant to a subject matter domain, thereby imparting andformalizing some of his or her knowledge about the domain. An embodimentof an associative relevancy knowledge profiler of the present inventionmay also allow a user to create a profile by modifying or restrictingthe known reference sets and windowing the results from the associationas a user might refine any other analysis algorithms and for relatedevaluation purposes, but here the user in effectively modifying orrestricting knowledge, often to improve or clarify the knowledge, or tofurther define the knowledge. Further embodiments of the presentinvention can be used to define a profile that can subsequently be usedby the user who creates the profile and/or use by others. A user profilemay be usable in various manners depending upon, for example, rightsmanagement permissions and restrictions for a user. For example, theoriginal author of the profile may be permitted to revise the originalprofile, but another user may only be able to further restrict theoriginal profile. Similarly, a user profile may be usable in variousdomains and/or with different data and information sources, such asthose data sources available to a particular user. For example, anemployee of a company may be able to use a profile with the data andinformation sources available to the company, such as proprietary dataand information available to, obtained by, and/or generated by thecompany.

Embodiments of the present invention are provided for an associativerelevancy knowledge profiling system that includes a user interfacemodule configured to generate a user interface for presentation on adisplay screen and to receive user input; a known reference set moduleconfigured to provide at least two available known reference sets and topermit selection of at least two known reference sets by a selectiveuser input received by said user interface module; an association moduleconfigured to create an association between two of said available knownreference sets based upon an associative user input received by saiduser interface module, to generate a knowledge construct based upon saidassociation and said two of said available known reference sets, and togenerate a profile comprising said association between said two of saidavailable known reference sets; and a storage module configured to storesaid profile and to provide said profile for subsequent use.

One embodiment of a system of the present invention may also include arestriction module configured to modify said profile to impose arestriction upon at least one of said two of said available knownreference sets based upon a restricting user input received by said userinterface module. Another embodiment of a system of the presentinvention may also include a preferencing module configured to modifysaid profile to impose a viewing preference upon any use of said profilebased upon a preference user input received by said user interfacemodule.

Embodiments of the present invention are also provided for a method ofassociative relevancy knowledge profiling that include providing atleast two known reference sets; selecting at least two of said knownreference sets up, including at least a first known reference set and asecond known reference set; creating at least one association betweentwo of said at least two of said known reference sets to generate atleast part of an associative relevancy knowledge profile, wherein one ofsaid at least one association is between said first known reference setand said second known reference set; and storing said associativerelevancy knowledge profile.

One embodiment of a method of the present invention also includesinputting at least one reference set to be one of said known referencesets. Another embodiment of a method of the present invention alsoincludes modifying said an associative relevancy knowledge profile. Afurther embodiment of a method of the present invention also includesselecting at least one data source to evaluate using said profile andperforming a discovery process operation upon said at least one datasource using said profile. Yet a further embodiment of a method of thepresent invention also includes storing a plurality of associativerelevancy knowledge profiles to create a knowledge repository. And yet afurther embodiment of a method of the present invention also includesselecting one of said profiles in said knowledge repository, to identifya selected profile; selecting at least one data source to evaluate usingsaid selected profile; and performing a discovery process operation uponsaid at least one data source using said selected profile. And yet afurther embodiment of a method of the present invention also includescalculating a charge for use of said selected profile or calculating acharge for performing a discovery process operation upon said at leastone data source.

Embodiments of the present invention are also provided for a computerprogram product comprising a computer-readable storage medium havingcomputer-readable program code portions stored therein and providing forassociative relevancy knowledge profiling, the computer, the computerprogram product including a program code portion configured forproviding at least two known reference sets; a program code portionconfigured for selecting at least two of said known reference sets up,including at least a first known reference set and a second knownreference set; a program code portion configured for creating at leastone association between two of said at least two of said known referencesets to generate at least part of an associative relevancy knowledgeprofile, wherein one of said at least one association is between saidfirst known reference set and said second known reference set; and aprogram code portion configured for storing said associative relevancyknowledge profile.

One embodiment of a computer program product of the present inventionalso includes a program code portion configured for inputting at leastone reference set to be one of said known reference sets. Anotherembodiment of a computer program product of the present invention alsoincludes a program code portion configured for modifying said anassociative relevancy knowledge profile. A further embodiment of acomputer program product of the present invention also includes programcode portions configured for selecting at least one data source toevaluate using said profile and for performing a discovery processoperation upon said at least one data source using said profile. Yetanother embodiment of a computer program product of the presentinvention also includes a program code portion configured for storing aplurality of associative relevancy knowledge profiles to create aknowledge repository. And yet another embodiment of a computer programproduct of the present invention also includes program code portionsconfigured for selecting one of said profiles in said knowledgerepository, to identify a selected profile; for selecting at least onedata source to evaluate using said selected profile; and for performinga discovery process operation upon said at least one data source usingsaid selected profile.

These characteristics, as well as additional details, of the presentinvention are described below. Similarly, corresponding and additionalembodiments of associative relevancy knowledge profiling architecturesand related systems, methods, and computer program products of thepresent invention are also described below.

BRIEF DESCRIPTION OF THE DRAWING(S)

Having thus described embodiments of the invention in general terms,reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram of a framework that would benefit fromembodiments of the present invention;

FIG. 2 is a block diagram of a network framework that would benefit fromembodiments of the present invention;

FIG. 3 is a functional block diagram of an embodiment of an associativerelevancy knowledge profiling architecture of the present invention;

FIG. 4 is a flow diagram for performing associative relevancy knowledgeprofiling according to an embodiment of the present invention;

FIG. 5 is a flow diagram for using associative relevancy knowledgeprofiles generated according to an embodiment of the present invention;and

FIG. 6 is a schematic block diagram of an entity capable of operating asa computing system in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all embodiments of the invention are shown. Indeed, embodimentsof the present invention may be embodied in many different forms andshould not be construed as limited to the embodiments set forth herein;rather, these embodiments are provided so that this disclosure willsatisfy applicable legal requirements. Like reference numbers refer tolike elements throughout.

It will be appreciated from the following that many types of devices maybe used with the present invention, including without limitation such asdevices as servers and other shared computing systems, personalcomputers, laptop computers, and mobile stations such as handheld dataterminals and personal data assistants (PDAs). It will also beappreciated that embodiments of the present invention may beparticularly useful with use in conjunction with search engines andinference engines. However, embodiments of the present invention are notlimited to use applications involving search engines and inferenceengines, but may be employed with a variety applications related toevaluating data and/or information. Similarly, although many of theexamples used herein relate to the medical and healthcare environment,it will be appreciated that embodiments of the present invention may beused with any subject matter domain, individually and also collectivelywith other subject matter domains.

It should also be noted that a domain need not be defined merely bysubject matter, such as medicine or sports, but may also be defined bycategorical distinctions and other similarities related to evaluatingand analyzing the subject matter of the domain, such as a domain relatedto all judicial opinions authored by a particular court or by aparticular judge. Further, a domain may be defined in relation to adesire to exclude certain data and information, such as a domain relatedto the ability to exclude adult-oriented content from the evaluation oranalysis. Defining a domain merely refers to a user representing himselfor herself as a domain expert for relevant associations related to thedata and information represented by an related to any known referencesets; hence, embodiments of the present invention provide associativerelevancy knowledge profiling architectures and related systems,methods, and computer program products.

As used herein, the terms “data” and “information” may be usedindependently, but collectively or independently generally refer to anytype of content. Data is considered generally to be factual, oftenencompassing statistics with an emphasis on numerical values. Andinformation, by comparison to data, is considered generally to notemphasize numerical values, but rather to emphasize the written word.The distinctions between what kind of content is represented by data andwhat kind of content is presented by information is not specificallyrelevant to the present invention, and, thus, any reference hereinsolely to data or information without a particular distinction to theexclusion of the other is intended to represent data and informationcollectively such that as used herein, the terms “data” and“information” may be used interchangeably to refer any type of content.However, the separate terms data and information are generally usedindependently to be more descriptive of various types of content andalso to distinguish the content of data and information from“knowledge.”

As used herein, the term “knowledge” refers to a higher order ofinformation, or context in which to interpret information, that impartsan awareness or comprehension of a subject matter domain by a user(referred to herein as a domain expert), and is inclusive of at leastone association between two known reference sets, also referred to asassociative knowledge. Knowledge, as used herein, is not merely someadditional piece of data or information, but is a special form ofinformation that is generated by a domain expert through awareness andcomprehension of a domain. Knowledge may be found in many forms, but isoften nonfunctional, such as embedded in the form in which it is found.For example, literature typically contains some representation ofknowledge as the writing is typically created with the subjectiveinterpretation of the author. But, as embedded in the literature, theknowledge simply becomes part of the information and is nonfunctional.Embodiments of the present invention make use of knowledge that isfunctional, i.e., knowledge that can be represented in a profile andused for a discovery process. Embodiments of the present inventionprovide a vehicle in which a user can define at least some of his or herknowledge that can then be used to create, store, and further refine afixed representation of the user's knowledge in relation to knownreference sets, i.e., known subject matter of a domain. As defined andused herein, knowledge refers to associations between known referencesets. Embodiments of the present invention represent knowledge using atleast one association between known reference sets premised upon anotion that knowledge is appropriately represented through theassociative nature of the human interpretation of known reference setsfor a domain. Different types of associations are contemplated forknowledge, including, for example, semantic enrichment such as byassociating one or more elements of a known reference set with one ormore elements of an external data set; cross maps; table joins;computational restrictions and like rules; fixed, linear, and non-linearcomputational modifications and/or algorithms; and other associationsbetween known reference sets that imparts an awareness or comprehensionof the subject matter domain by a user to permit the user to formalizehis or her own conclusions, theories, anticipations, interpretations,impressions, and other preconceived notions about the domain asrepresented by the known reference sets and the user's associationsbetween the known reference sets, thereby permitting the creating of afunctional construct based upon the association.

As used herein, the term “known reference set” refers to a construct ororganized structure of data, information, associations, or othercontent. Preferably, a known reference set will represent an existingdataset agreed to, understood by, and validated by peers in a particulardomain, such as standards set by peer groups or organizations, but aknown reference set may also be a dataset that has not been agreed toand/or validated for a particular domain, but may merely be, forexample, a dataset generated by a domain expert or other user generatingan associative relevancy knowledge profile. Example known reference setsinclude: a list of vocabularies, such as a medical dictionary; a list ofidentifiers and identified items, such as the ICD9 classification ofdiseases and injuries and corresponding codes and the ICD9classification of procedures and corresponding codes; a list ofinformation such as a drug directory listing ingredients of drugs; anassociated list of information, such as a cross-reference chart betweenone classification system and another proprietary classification system;a dataset, such as Boolean or SQL search results; and a precompute datasource, such as the resulting dataset from running an inferencealgorithm against a set of articles, the resulting data set fromimplementing an associative relevancy knowledge profile on an initialdataset, or any other existing or generated dataset. Different types ofstructures are contemplated for known reference sets, including, forexample, a single level hierarchy; multiple level hierarchies such asfor a lexicon, taxonomy, ontology, thesaurus, and index; a predefinedassociated listed information or association construct such as a crossmap or table joins; and enriched and/or refined known or createdreference sets such as a proprietary reference set that is asemantically enriched dataset generated by supplementing a knownreference set with additional proprietary information of a company.

Further, it should be noted that embodiments of the present inventionare not search engines or inference engines, but are intended to providea separate type of system that may be used to improve upon searchengines and inference engines. Although embodiments of the presentinvention are a separate technology designed to provide additionalcapabilities and functionalities that are not known to exist, it may beuseful to think of an embodiment of the present invention as an add-onfor improving a search engine or inference engine, or a relatedprecursor system to using a search engine or inference engine.

Embodiments of the present invention provide advances in the generalfield of information retrieval by facilitating the ability for users tobuild a structure (profile) that represents at least some of the usersknowledge and thereby allows use of the structure (profile) for use wheninvestigating other resources (data sources). A profile (also referredto as a knowledge profile or discovery profile) provides a domain expertthe ability to build an expert system that incorporates existingknowledge of the domain expert that can be used for a discovery process.A profile provides a perspective of how a domain expert views a domainand represents at least some of the knowledge of the domain expert by atleast one association between two known reference sets for the domain.In addition to at least one association between two known reference setsfor the domain, the perspective of a profile may include, for example,additional selections, restrictions, rankings, and other preferencesthey represent how the domain expert views or recommends to view theconstruct of an association between two known reference sets for thedomain and/or the results of performing a discovery process operationupon one or more datasets using the profile. As such, a profilegenerally provides a perspective with both an association and how toview the association or results of the association.

Referring now to FIG. 1, a block diagram of a computing framework thatwould benefit from embodiments of the present invention is shown. At thecenter of the system is a computing system 22, such as a personalcomputer or server. Shown connected to the computing system 22 areseveral additional elements that may be separately connected to thecomputing system 22, interconnected by communications with the centralcomputing system 22, were part of the computing system 22. One or moreknown reference sets 26 are available to the computing system 22. Dataand/or information 24 representing existing content may be available tothe computing system 22. A profile database 28 for storing associativerelevancy knowledge profiles and referred to herein as a knowledgerepository may be available to the computing system 22. A user interface30, such as a web-based ASP interface or standalone application, may beavailable to the computing system 22. And a search engine 18 (orinference engine, or the like) may also be available to the computingsystem 22. The particular arrangements, connections, and physicallocalities of all of these elements are not specific to the presentinvention, but simply that the elements required for a specificembodiment are available to the computing system. For example, FIG. 2 isa block diagram of a network framework that would benefit fromembodiments of the present invention. Unlike the diagram of FIG. 1,which is representative of a more local or direct configuration, thediagram of FIG. 2 is representative of embodiments of the presentinvention which rely upon a network connection such as the Internet foravailability and communication between elements of the system. Forexample, as shown in FIG. 2, each of the elements of the system may beseparately located in interconnected through the Internet 20 foursubstantiating an embodiment of the present invention.

Systems, methods, and computer program products of embodiments of thepresent invention will be primarily described in conjunction with localor direct configuration applications, such as generally represented byFIG. 1, and some networked configuration applications, such asrepresented in FIG. 2. It should be understood, however, that systems,methods, and computer program products of embodiments of the presentinvention can be utilized in conjunction with a variety of otherconfigurations with various means of communication and connection. Forexample, systems, methods, and computer program products of embodimentsof the present invention can be utilized in conjunction with wirelineand/or wireless connections and in local/direct and networkedapplications. For example, wired connections may include any one or moreof an Ethernet connection, a serial (RS-232) connection, a parallel(IEEE 1284) connection, a USB connection, a SCSI connection, and othermeans of achieving a wired or contact electronic connection capable ofproviding communications between two computing devices. Wirelessconnections may include any one or more of radio frequency (RF),Bluetooth (BT), infrared (IR, IrDA), wireless LAN (WLAN, IEEE 802.11),WiMAX (IEEE 802.16), ultra wideband (UWB, IEEE 802.15), microwave (μ),ultraviolet (UV), and other means of achieving a wireless, non-contactelectronic connection capable of providing communications between twocomputing devices. Further, network applications can be of any variety,including without limitation a Local Area Network (LAN), a Campus AreaNetwork (CAN), a Metropolitan Area Network (MAN): a Wide Area Networks(WAN), an intranet, and an extranet.

FIG. 3 is a functional block diagram of an embodiment of an associativerelevancy knowledge profiling architecture of the present invention withthe functional schematics of computing system 22 shown in detail.Embodiments of the present invention provide a tool that allows a user(domain expert) to develop an associative relevancy knowledge profile,also referred to as a knowledge discovery profile, or simply as aprofile. A selection of five known reference sets 26 are shown as beingincluded in the computing system: a vocabulary 26A, a list ofinformation 26B, an associated list of information 26C, a set of searchresults 26D, and a precompute 26E. By using known reference sets, a useris able to take advantage of using a reference framework and ontologiesthat ordinarily are common to others in a particular domain, such thatother users who might desire to use a profile created by a domain expertwill be familiar with the subject matter of the known reference setsand, thus, able to examine and presumably comprehend the knowledgeimparted by the domain expert about the domain. At least one associationis created by a domain expert to memorialize an association between twoof the known reference sets 26. The association defines an associatedrelationship between known reference sets as perceived by a domainexpert, and as the domain expert believes may be relevant tounderstanding the subject matter of the domain. This provides a domainexpert with the ability to impart at least some of his or her knowledgeabout the domain in a formalized manner that can later be used by thedomain expert or others to analyze and evaluate the subject matter ofthe domain. For example, domain expert association construct 32A isidentified as an association between the vocabulary known reference set26A and the list of information known reference set 26B. An additionaldomain expert association construct 32B is identified as an associationbetween the associated list of information known reference set 26C andthe precompute known reference data set 26E.

A profile is domain dependent, at least in so far as the profile reliesupon known reference sets which, at least in theory, are derived from orthemselves define a domain; and preferably, a profile is created to bedomain dependent such that the known reference sets, knowledge, andviewing preferences, respectfully, selected, imparted, and defined bythe domain expert are designed so that the profile is particularized andeffective for discovery in the domain. But a profile need not bespecific to a certain subject matter of the domain, such that a domainmay be exceptionally broad and inclusive of numerous subject matters.And, as noted above, a domain need not be defined merely by subjectmatter, such as medicine or sports, but may also be defined bycategorical distinctions and other similarities related to evaluatingand analyzing the subject matter of the domain or defined in relation toa desire to exclude certain data and information.

It should be noted that an aspect of the present invention, which is notapparent from the representation of FIG. 3, is the scalability ordimensionality of the associative relevancy knowledge profilingarchitecture. Each of the known reference sets may be considered adimension. Thereby, the more reference sets that are included inembodiment, the higher the dimensionality of the associative relevancyknowledge profiling architecture. For example, an embodiment with onlytwo known reference sets may be considered a two dimensionalarchitecture, and any association construct between the two knownreference sets may be thought of as the intersection of two axes (ordimensions) of the two known reference sets. Similarly, for example, anembodiment with five known reference sets, as shown in FIG. 3, may beconsidered a fifth dimensional architecture with possible associationsbetween any of the five axes (or dimensions) of the five known referencesets. As such, the present invention provides a framework in which theassociative relevancy knowledge profiling architecture is scalable overany number of dimensions without limitation.

As is used by convention herein throughout, an element of a figure maybe shown in dashed lines representing an optional element of anembodiment of the present invention. For example, only one associationis necessary for an embodiment of the present invention, so theadditional domain expert association construct 32B is shown in dashedlines. However, because various embodiments the present invention mayemploy different capabilities and functionalities, one or more elementsshown in dashed lines may be a required element of a specific embodimentof the present invention.

FIG. 3 also shows that a domain expert can modify and refine the profilebeing created, such as by including a restriction or selection upon oneor more of known reference sets before an association is made, such asthe restrictions/selection 34 shown modifying the vocabulary knownreference set 26A before the domain expert creates the association 32A.For example, a known reference set may be restricted for a profile toonly a desired portion of the known reference set, or a known referenceset may be restricted by computationally ranking or otherwise modifyingsome portion of the known reference set, such as semantically enrichinga portion of the known reference set.

Similarly, FIG. 3 also shows that a domain expert can also impartrestrictions upon the associative relevancy knowledge profile regardinghow the profile is used and the outcome of results from using theprofile, as shown at block 36. This functionality is referred to hereinas modifying viewing preferences or “windowing” the profile. Use of anexisting profile may entail modification of the profile, such asrefining a profile by editing the original profile. Modification of theprofile may also involve a user (either the original domain expert or asubsequent user) enhancing a profile by adding additional knowledge toan existing profile by building upon the existing profile. Modificationof the profile may also involve a user further restricting a profile,such as restrictions of a user without rights to edit the existingprofile or add additional knowledge to the existing profile. A domainexpert can, thus, define an association to create a constructrepresenting knowledge, and then restrict the construct to form areference in which to view the knowledge construct, i.e., a reference inwhich to view the one association between two known reference sets, orany number of associations defined for the profile. Thus, not only doembodiments of the present invention allow a user to impart knowledgeinto a profile to store his or her knowledge about a domain, such asknowledge to obtain desired data and information, but also to store hisor her perspective on how to evaluate data and information and also torepresent the results of a discovery process according to how the domainexpert believes the results should may be viewed, explored, and/orevaluated. These further restrictions upon the profile may include manypreferences, which might be typical for viewing any type of content,including, for example, refining search results. Viewing preferences mayinclude, for example, rankings or biasings, filters, a selectivefilters, clustering, and computational clustering. Even such simple andsomewhat administrative preferences may be included as part of aprofile, such as the number of results that will be visible in a window,the type and/or amount of content that may be provided for a result, andeven the style of the graphical user interface presenting results. Ineffect, a domain expert is able to formalize his or her knowledge aboutthe domain not only by defining associations between known referencesets but also by such actions as modifying the known reference sets andmodifying the manner in which a profile will operate to be used on oneor more data or information sources. And, as such, a domain expert isable to add his or her human element to a profile from beginning to endbecause a profile is, in essence, an attempt to capture the humanelement as it relates to discovery based upon knowledge.

Also shown in FIG. 3 is a data/information server 24 that is connectedto or in communication with computing system 22 to provide one or moredata and information sources for evaluation using a profile generated bythe computing system 22. As such, the data/information server 24provides subject matter of the domain that can be analyzed according tothe knowledge of a profile generated by the computing system 22. The useof a profile, by performing an operation upon one or more data andinformation sources using the profile, such as searching a data source,is referred to herein as a discovery process and usually refers to theevaluation and/or analysis of the one or more data sources in accordancewith the knowledge of the profile. A discovery process may involverunning a profile upon (over or against) a single data source ormultiple data sources. For example, a user may be able to select aprofile and run the profile in a discovery process over one or more of aplurality of data sources. Typically, a plurality of data/informationsources may be used for performing a discovery process operation using aprofile generated by the computing system 22, and, typically more thanone data/information servers may be involved for providing the pluralityof data/information sources.

Also connected to the computing system 22 is a profile database 28,referred to herein as a knowledge repository. An embodiment of thepresent invention may be used to create a knowledge repository bycreating and/or storing multiple associative relevancy knowledgeprofiles and maintaining the profiles in storage for subsequent use, andmay also involve modification/revision and/or further restriction of thestored profiles. Knowledge repositories may be created with multipleprofiles for use with a single data source or for use with multiple datasources. Knowledge repositories may be created where domain expertsstore and publish/share their profiles for use (and/or for re-use) oftheir knowledge by others, such as for other researchers, otheremployees, or any other type or classification of users. And anyvariation of access management, editorial rights management, and usemanagement may be employed for operating a knowledge repository,including, for example, such standard administrative functions ascreating user accounts with defined rights management values thatdetermine what rights a user has to view, use, modify, refine, etc. oneor more profiles in the knowledge repository. For example, certain usersmay have access rights to access all or only a subset of the profilesstored in a knowledge repository while other users may only have accessrights to access a limited subset of all of the profiles or a differentsubset of the profiles. Similarly, certain users may have unlimitededitorial rights to modify, enhance, refine, etc. profiles stored in aknowledge repository while other users may have limited or no editorialrights. For example, those users with limited editorial rights may berestricted to use an existing profile that can only be furtherrestricted, but cannot be modified, similar to a pre-populated search orinference template that cannot be modified. But a user with editorialrights may be able to modify an existing profile. And use of profilesmay be managed in any variety of manners, including fee-based rightsmanagement such as to charge users on a transaction basis for use ofprofiles. And profiles may vary in cost such as where certain profilesmay be free, certain profiles may have a low cost basis, and otherprofiles may have a high cost basis. Any one or more subscription basedservice arrangements may be established for an associative relevancyknowledge profiling system or a knowledge repository. In one embodimentof the present invention, a knowledge profile may be used by multiplecompanies to facilitate a joint venture and/or sharing of researchcapabilities between the companies. For example, two or more companiesmay share an associative relevancy knowledge profiling system thatpermits the two companies to each offer their respective proprietaryknown reference sets for use in creating profiles, which may or may notbe shared between the companies. In addition, or alternatively, two ormore companies may share a knowledge repository that permits the twocompanies to each offer their respective profiles for use by the othercompany to use with the company's proprietary data sources and/or withproprietary data sources of the other company.

FIG. 3 also shows a search server 18 connected to or in communicationwith the computing system 22. The search server 18 (i.e., search engine,inference engine, or the like) may be used for performing a discoveryprocess operation upon one or more data and information sources usingthe profile. For example, a search engine may be used to search a datasource using a profile according to the knowledge of the profile, ratherthan, for example, only using search terms and operators such as for aBoolean search. Similarly, an inference engine may be used to performinference algorithms according to the knowledge of a profile.

And a user interface 30 is shown in FIG. 3 connected to or incommunication with the computing system 22. A user interface generallyis an integral software module with an associative relevancy knowledgeprofiling software module operating on the computing system 22, but mayalternatively be separate from the computing system 22 and/or anassociative relevancy knowledge profiling software module operating onthe computing system 22. For example, one or more user interfaces may beprovided separate from the computing system 22, such as where a companyprovides its own proprietary profiling user interface through thecompany intranet for generating profiles using an associative relevancyknowledge profiling software module operating on the computing system22. Similarly, a knowledge repository user interface may be provided bya separate application, such as associated with a search server or otherservice apart from the computing system 22. A user interface 30 may bedesigned in any number of manners, including providing a graphical userinterface (GUI) for presentation on a display screen and that receives auser input for an associative relevancy knowledge profiling system. Oneor more user interfaces may be designed for the creation of profiles,the maintenance and use of a knowledge repository, and use of profiles.For example, a specially designed user interface may be created thatalso implements additional technologies that may be similarly useful inusing a profile according to an embodiment of the present invention,such as a profile use interface that also implements the ThinkMap™visualization technology of ThinkMap Corporation of New York, N.Y., fordisplaying and viewing, navigating and exploring, and/or evaluatingresults of a discovery process.

An optional enhancement to an embodiment of the present invention may beusing fixed and/or dynamic “containers” for known reference sets,associations, rules, and data sources. A container is a predefinedconstraint for an aspect of an associative relevancy knowledge profilingsystem. For example, a known reference set container may be a predefinedconstraint that is used with one or more known reference sets includedas part of a profile. Containers may use fixed constraints, such as aconstant numerical multiplier that may be used, for example, todynamically bias a weighted importance of a known reference set.Alternatively, containers may use dynamic constraints, such as avariable numerical multiplier that may be used, for example, to bias aweighted importance of a known reference set. By using a dynamicconstraint, a profile may be created that has the subsequent ability todynamically adjust to a change in the domain, such as a change to aknown reference set or any other influence on the dynamic constraint. Adynamic constraint may be updated manually, automatically, orperiodically. A manual update may occur, for example, if a user selectsa profile with a dynamic constraint for a container, the system alertsthe user of the dynamic constraint and requesting the user to confirm ordeny updating the dynamic constraint, and the user chooses to update thedynamic constraint. An automatic update may occur, for example, eachtime the profile is selected for use or modification. A periodic updatemay occur, for example, at a predefined frequency of time, such as tomaintain profiles in a knowledge repository that are generallyup-to-date and current for the domain, regardless whether or not theprofile has been selected for use or modification, and, thus, a profilemay not require further updating at the time of use or modification by auser because it is constantly (periodically) kept up to date and currentfor the domain. Use of fixed and dynamic constraints for containersallows a domain expert the ability to define, for example, if certainaspects of a profile are important and should change or should notchange. Containers may be used with any aspect of a profile, including,for example, to constrain and/or modify a known reference set, anassociation, a construct from an association, or a viewing preference.

FIG. 4 is a flow diagram for performing associative relevancy knowledgeprofiling according to an embodiment of the present invention.Associative relevancy knowledge profiling, also referred to as thecreation of a profile, is identified as starting at block 100. A userselects or chooses two or more known reference sets, as shown at block102. Optionally, the user may further refine the profile, such as byrestricting one or more of these selected known reference sets, as shownat block 104. A user and then creates at least one association betweentwo of the known reference sets, as shown at block 106. Optionally, theuser may further refine the profile, as shown at block 108 by theselecting view properties. The selection of view properties refers toany number of modifications that a user may impose upon a profile,including, for example, filters, rule sets, rankings, and otherpreferences that the user imposes to control the manner in which eitherthe profile operates or the results of using the profile may be viewed.Such further refinements are intended to permit the user the ability toimpart upon the profile, not only the basic structure of the usersknowledge in the form of associations between known reference sets, butalso the user's knowledge encompassed in the form of how the userperceives the profile should be used and/or how the user perceives theresults of using the profile should be viewed. Optionally, the user mayfurther refine the profile by pre-selecting one or more data and/orinformation sources that should be or are intended to be used with theprofile for performing a discovery process, as shown at block 110. Thismay also involve identifying a particular type of required or intendeddiscovery process for the profile, potentially even to the extent ofidentifying a required or intended system (search engine or inferenceengine) to perform the discovery process on the one or more data and/orinformation sources using the profile. Typically, an embodiment ofpresent invention will permit the user to store the profile, as shown atblock 112, such as in a knowledge repository, which permits the profileto be used and/or re-used at a subsequent time by the domain expert oranother user. Further, typically the user will then perform a discoveryprocess operation on one or more data and/or information sources usingthe profile, as shown at block 114. And when knowledge profiles arestored, such as to be shared in a knowledge repository, often theprofile is not restricted to a particular one or more data sources, butpermits the selection or choice of the one or more data sources that isanalyzed with the profile to occur after selection of the profile forsubsequent use.

FIG. 5 is a flow diagram for using associative relevancy knowledgeprofiles generated according to an embodiment of the present invention.A discovery process is identified as starting at block 200. A userselects or chooses a knowledge profile, as shown at block 202. Dependingon the capabilities of the system and/or the rights of the user, theuser may be able to revise the knowledge profile, as shown at block 204or to further enhance or restrict the knowledge profile, as shown atblock 206. The user also selects or chooses the data and/or informationsource or sources, as shown at block 208 that the user desires to runagainst the knowledge profile. And then the user is able to perform adiscovery process operation upon the data and/or information source(s)using the knowledge profile, as shown at block 210. A discovery processmay be a search, evaluation, or other analysis of the data source(s)based upon the knowledge and other user preferences built into theprofile. And the results of a discovery process, or any other search,evaluation, or analysis operation may be used as a known reference setfor creating another profile.

FIG. 6 is a schematic block diagram of an entity capable of operating asa computing system in accordance with an embodiment of the presentinvention. Although shown as separate entities, such as in FIG. 1, insome embodiments, one or more entities may support one or more of acomputing system, known reference set data source(s), data and/orinformation source(s), a profile database (knowledge repository), a userinterface, and/or a search engine or inference engine, logicallyseparated but co-located within the entit(ies). For example, a singleentity may support a logically separate, but co-located, computingsystem and knowledge repository. Also, for example, a single entity maysupport a logically separate, but co-located computing system and searchengine. The entity capable of operating at least as a computing system22 includes various means for performing one or more functions inaccordance with exemplary embodiments of the present invention,including those more particularly shown and described herein. It shouldbe understood, however, that one or more of the entities may includealternative means for performing one or more like functions, withoutdeparting from the spirit and scope of the present invention. Moreparticularly, for example, as shown in FIG. 6, the entity can include aprocessor, controller, or like processing element 634 connected to amemory 636. It is understood that the processor 634 may include thecircuitry required for implementing the logic functions of a computingsystem 22 for an embodiment of the present invention. For example, theprocessor 634 may be comprised of a digital signal processor device, amicroprocessor device, and various analog-to-digital converters,digital-to-analog converters, and other support circuits. The controland signal processing functions of a computing system may be allocatedbetween these devices according to their respective capabilities.Further, the processor 634 may include the functionality to operate oneor more software programs, which may be stored in memory 636.

The memory 636 can comprise volatile and/or non-volatile memory, andtypically stores content, data or the like, either or both on atemporary basis or for a longer term and/or permanent basis. Forexample, the memory typically stores content created by, transmittedfrom, and/or received by, the entity. Also for example, the memorytypically stores computer program code, such as for operating systemsand client applications, for the processor to perform steps associatedwith operation of the entity in accordance with embodiments of thepresent invention. Memory 636 may be, for example, read only memory(ROM), random access memory (RAM), a flash drive, a hard drive, and/orother fixed data memory or storage device. For example, non-volatilememory may include a flash memory, or the like, such as available fromthe SanDisk Corporation of Sunnyvale, Calif., or Lexar Media Inc., ofFremont, Calif. The memories can store any of a number and amount ofdata and information, including known reference sets, profiles, and datasources.

As described herein, the computer program product module(s) and/orapplication(s) may each comprise software operated by the respectiveentities. It should be understood, however, that any one or more of themodules and applications described herein can alternatively comprisefirmware or hardware, without departing from the spirit and scope of thepresent invention. Generally, then, a computing system may include oneor more logic elements for performing various functions of one or moremodule(s) and/or application(s). As will be appreciated, the logicelements can be embodied in any of a number of different manners. Inthis regard, the logic elements performing the functions of one or moreclient applications can be embodied in an integrated circuit assemblyincluding one or more integrated circuits integral or otherwise incommunication with a respective entity (i.e., computing system, knownreference set data source(s), data and/or information source(s), aprofile database (knowledge repository), a user interface, and/or asearch engine or inference engine, etc.) or more particularly, forexample, a processor 634 of the respective entity. The design ofintegrated circuits is by and large a highly automated process. In thisregard, complex and powerful software tools are available for convertinga logic level design into a semiconductor circuit design ready to beetched and formed on a semiconductor substrate. These software tools,such as those provided by Avant! Corporation of Fremont, Calif. andCadence Design, of San Jose, Calif., automatically route conductors andlocate components on a semiconductor chip using well established rulesof design as well as huge libraries of pre-stored design modules. Oncethe design for a semiconductor circuit has been completed, the resultantdesign, in a standardized electronic format (e.g., Opus, GDSII, or thelike) may be transmitted to a semiconductor fabrication facility or“fab” for fabrication.

In addition to the memory 636, the processor 634 can also be connectedto at least one interface or other means for displaying, transmittingand/or receiving data, content or the like. In this regard, theinterface(s) can include at least one communication interface 638 orother means for transmitting and/or receiving data, content or the like.For example, the communication interface(s) may include a firstcommunication interface for connecting to a first wireless connectedentity and/or network and a second communication interface forconnecting to a second wired connected entity and/or network. When anentity provides wireless communication to operate wirelessly with aconnected entity or to operate in a wireless network, such as aBluetooth network, a wireless network, or other mobile network, theprocessor 634 may operate with a wireless communication subsystem of theinterface 638. In addition to the communication interface(s), theinterface(s) can also include at least one user interface that caninclude one or more earphones and/or speakers 639, a display 640, and/ora user input interface 642. The user input interface, in turn, cancomprise any of a number of devices allowing the entity to receive datafrom a user, such as a microphone, a keypad, a touch display, a joystickor other input device. One or more processors, memory, storage devices,and other computer elements may be used in common by a computer systemand subsystems, as part of the same platform, or processors may bedistributed between a computer system and subsystems, as parts ofmultiple platforms. It should be understood that an embodiment of acomputing system of the present invention may include alternative meansand/or additional supporting software and/or hardware for performing oneor more like functions, without departing from the spirit and scope ofthe present invention. More particularly, for example, a wirelessconnection for communication interface 638 might include an antenna, atransmitter, a receiver, and a controller for the wireless operation.

If the entity is, for example, an associative relevancy knowledgeprofiling system, the entity may also include a user interface module682, a known reference set module 684, an association module 686, astorage module 688, a restriction module 690, and a view preferencingmodule 692 connected to the processor 634. These modules may be softwareand/or software-hardware components, such as computer-readable programcode portions stored on a computer-readable storage medium, and one ormore modules may be combined into a single module. For example, a userinterface module 682 may include software and/or software-hardwarecomponents capable of and configured to generate a user interface forpresentation on a display screen and also to receive user input from adomain expert. A known reference set module 684 may include softwareand/or software-hardware components capable of and configured to provideat least two available known reference sets to the domain expert and topermit the selection of at least two known reference sets by the domainexpert according to a selective user input of the domain expert receivedby, for example, the user interface module 682. A selective user inputmay be, for example, highlighting the identification (name) of a knownreference set, selecting a radio button for a known reference set,checking a checkbox for a known reference set, or any similar means toindicate to the known reference set module 684 that the domain expertdesires a particular known reference set to be used. A known referenceset module 684 may also be capable of and configured to input at leastone known reference set as one of the available known reference sets,such as to permit the domain expert to create a known reference set, topermit the domain expert to upload a file representing a known referenceset, to retrieve a known reference set from memory, or a similar meansto provide the contents of a known reference set to the known referenceset module 684. Further, a known reference set module 684 may also becapable of and configured to identify the at least one known referenceset input by the known reference set module 684, such as based upon anidentifying user input of the domain expert received by, for example,the user interface module 682. Identifying user input may be, forexample, selecting a known reference set such as a known reference setinput by the known reference set module 684 from a list of availableknown reference sets, locating a known reference set on a local ornetwork storage device, or any similar means to indicate for the knownreference set module 684 that the domain expert desires a particularknown reference set to be used with the profile.

An association module 686 may include software and/or software-hardwarecomponents capable of and configured to create an association betweentwo of the available known reference sets according to an associativeuser input of the domain expert received by, for example, the userinterface module 682. A created association may be, for example,semantic enrichment such as associating one or more elements of a firstknown reference set with one or more elements of a second knownreference set; a cross map between two known reference sets; a tablejoin between two known reference sets; one or more computationalrestrictions or like rules applied to one or more elements of a firstknown reference set and possibly also one or more elements of a secondknown reference set based upon one or more elements of the second knownreference set and possibly also one or more elements of the first knownreference set, respectively; a fixed, linear, or non-linearcomputational restriction/modification and/or numerical algorithmapplied to one or more elements of a first known reference set andpossibly also one or more elements of a second known reference set basedupon one or more elements of the second known reference set and possiblyalso one or more elements of the first known reference set,respectively; or a similar means of associating two known reference setsthat imparts an awareness or comprehension of the subject matter domainby a domain expert to permit the domain expert to formalize his or herown conclusions, theories, anticipations, interpretations, impressions,and other preconceived notions about the domain as represented by theknown reference sets and the domain expert's association(s) between theknown reference sets, thereby permitting the creating of a functionalconstruct based upon the association(s). Similarly, a createdassociation may be, for example, different from or in addition to anassociation creating a one-to-one correspondence linking between atleast two components (two or more elements and/or two or more groups ofelements) from two known reference sets, such as, in addition to or asan alternative to a cross map or table join, an association may becreated that provides semantic enrichment of a first known reference setbased upon one or more elements of a second known reference set. Theassociation module 686 may be further capable of and configured togenerate a knowledge construct based upon the association and the twoavailable known reference sets of the association. Further, theassociation module 686 may be further capable of and configured togenerate a profile comprising the association between the two availableknown reference sets of the association.

A storage module 688 may be capable of and configured to store a profileand to provide the profile for subsequent use. For example, a storagemodule 688 may be capable of and configured to store profiles generatedby the association module 686 in one or more memories for temporaryand/or more permanent storage and subsequent use. A storage module 688may also be capable of and configured to store profiles that may bepublished and/or shared, maintained, and subsequently used as part of aknowledge repository.

A restriction module 690 may be capable of and configured to modify aprofile to impose a restriction upon at least two known reference setsof an association of the profile, such as based upon a restricting userinput of the domain expert received by, for example, the user interfacemodule 682. A restricting user input may define a restriction that is,for example, a selection, a ranking, a filter, a semantic enrichment, acomputational restriction, a fixed, linear, or non-linear computationalrestriction/modification and/or numerical algorithm.

A preferencing module 692, also referred to as a view preferencingmodule, may be capable of and configured to modify a profile to impose aviewing preference upon any use of the profile, such as based upon apreference user input of the domain expert received by, for example, theuser interface module 682. A preference user input may define a viewingpreference that is, for example, a ranking preference, a quantitypreference, a content preference, or a filtering preference.

Similar other modules comprising software and/or software-hardwarecomponents may also be connected to the processor 634 including softwarecapable of performing one or more of the additionally describedfunctions and capabilities of an embodiment of the present inventionand/or a system related to an embodiment of the present invention.

Embodiments of the present invention are described with reference toblock diagrams and flowchart illustrations of methods, apparatus (andsystems), and computer program products according to embodiments of theinvention. In this regard, each block or step of a block diagram orflowchart, and combinations of blocks in a block diagram or flowchart,can be implemented by various means, such as hardware, firmware, and/orsoftware including one or more computer program instructions embodied incomputer-readable program code logic. As will be appreciated, any suchcomputer program instructions may be loaded onto a computer, includingwithout limitation a general purpose computer or special purposecomputer, or other programmable processing apparatus to produce amachine, such that the computer program instructions which execute onthe computer or other programmable processing apparatus create means forimplementing the functions specified in the block diagrams' andflowchart's block(s) or step(s).

Accordingly, blocks of the block diagrams and flowcharts supportcombinations of means for performing the specified functions,combinations of steps for performing the specified functions, andcomputer program instructions, such as embodied in computer-readableprogram code logic means, for performing the specified functions. Itwill also be understood that each block of the block diagrams andflowchart illustrations, and combinations of blocks in the blockdiagrams and flowchart illustrations, can be implemented by specialpurpose hardware-based computer systems which perform the specifiedfunctions or steps, or combinations of special purpose hardware andcomputer-readable program code logic means.

Furthermore, these computer program instructions, such as embodied incomputer-readable program code logic, may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable processing apparatus to function in a particular manner,such that the instructions stored in the computer-readable memoryproduce an article of manufacture including instruction means whichimplement the function specified in the block diagrams' and flowchart'sblock(s) or step(s). The computer program instructions may also beloaded onto a computer or other programmable processing apparatus tocause a series of operational steps to be performed on the computer orother programmable processing apparatus to produce acomputer-implemented process such that the instructions which execute onthe computer or other programmable processing apparatus provide stepsfor implementing the functions specified in the block diagrams' andflowchart's block(s) or step(s).

Provided herein are improved architectures, systems, methods, andcomputer program products that provide a user with the ability to definean association of data and/or information from known reference sets thatis perceived by the user as relevant to a subject matter domain, therebyimparting and formalizing some of the user's knowledge about the domain.An associative relevancy knowledge profiler may also allow a user tocreate a profile by modifying or restricting the known reference setsand windowing the results from the association as a user might refineany other analysis algorithms and for related evaluation purposes. Anassociative relevancy knowledge profiler may also be used to define auser profile that can be used by the user and others. A user profile maybe usable in various manners depending upon, for example, rightsmanagement permissions and restrictions for a user. Similarly, a userprofile may be usable in various domains and/or with different data andinformation sources, such as those data sources available to aparticular user.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

That which is claimed:
 1. An associative relevancy knowledge profilingsystem, comprising: at least one user interface module configured to:generate a user interface for presentation on a display screen; andreceive user input that represents a user's intention to create anassociation between reference sets; a known reference set moduleconfigured to provide at least two available known reference sets and topermit selection of at least two said available known reference sets andan association type by a selective user input received by said userinterface module, wherein the association type defines a type ofassociation between the at least two said available known referencesets; an association module, implemented by a machine, configured: inresponse to the at least one user interface module receiving the userinput, to select the association type from a plurality of associationtypes, and to create an association between two of said available knownreference sets, the association being of the selected association type,to generate a knowledge construct based upon said association and saidavailable known reference sets, and to generate a profile comprisingsaid association between two of said available known reference sets; anda storage module configured to store said profile and to provide saidprofile for subsequent use, wherein said profile is associated withinone or more containers each having at least one predefined constraintthat biases at least one of said available known reference sets.
 2. Thesystem of claim 1, wherein the known reference set module is furtherconfigured to input at least one known reference set as at least one ofsaid available known reference sets.
 3. The system of claim 2, whereinthe known reference set module is further configured to identify said atleast one known reference set based upon an identifying user inputreceived by said user interface module.
 4. The system of claim 1,wherein said association provides an association between said two ofsaid available known reference sets either different from or in additionto an association creating a one-to-one correspondence linking betweentwo components, one of each component in each of said available knownreference sets.
 5. The system of claim 1, wherein said association typeis selected from the group of: a semantic enrichment, a computationalrestriction, a fixed numerical modification, a linear numericalcomputation, and a non-linear numerical computation.
 6. The system ofclaim 1, further comprising a restriction module configured to modifysaid profile to impose a restriction upon at least one of said availableknown reference sets based upon a restricting user input received bysaid user interface module.
 7. The system of claim 6, wherein saidrestriction is selected from the group of a selection, a ranking, afilter, a semantic enrichment, a computational restriction, a fixednumerical modification, a linear numerical computation, and a non-linearnumerical computation.
 8. The system of claim 1, further comprising apreferencing module configured to modify said profile to impose aviewing preference upon any use of said profile based upon a preferenceuser input received by said user interface module.
 9. The system ofclaim 8, wherein said viewing preference is selected from the group of:a ranking preference, a quantity preference, a content preference, and afiltering preference.
 10. A method of associative relevancy knowledgeprofiling, comprising: providing at least two known reference sets;selecting at least two of said known reference sets, including at leasta first known reference set and a second known reference set; creating,by a machine, at least one association between two of said knownreference sets to generate at least part of an associative relevancyknowledge profile, wherein creating said at least one associationcomprises: receiving a user input wherein the user input represents auser's intention to create an association between said first knownreference set and said second known reference set and an associationtype for the association; and in response to receiving said user input,creating said association with the association type, wherein saidassociation is one of said at least one association; and storing saidassociative relevancy knowledge profile, wherein said associativerelevancy knowledge profile is associated within one or more containerseach having at least one predefined constraint that biases at least oneof said available known reference sets.
 11. The method of claim 10,further comprising modifying said associative relevancy knowledgeprofile.
 12. The method of claim 11, wherein modifying said associativerelevancy knowledge profile comprises creating at least one restrictionupon said association between said first known reference set and saidsecond known reference set.
 13. The method of claim 11, whereinmodifying said associative relevancy knowledge profile comprisescreating at least one viewing preference for presenting results from useof said associative relevancy knowledge profile.
 14. The method of claim11, wherein modifying said associative relevancy knowledge profile:occurs after storing said associative relevancy knowledge profile; andcomprises modifying said association between said first known referenceset and said second known reference set.
 15. The method of claim 14,wherein modifying said associative relevancy knowledge profile comprisesmodifying at least one viewing preference for presenting results fromuse of said associative relevancy knowledge profile.
 16. The method ofclaim 10, further comprising: selecting at least one data source toevaluate using said associative relevancy knowledge profile; andperforming a discovery process operation upon said at least one datasource using said associative relevancy knowledge profile.
 17. Themethod of claim 10, further comprising: selecting one of saidassociative relevancy knowledge profiles in said knowledge repository,to identify a selected profile; selecting at least one data source toevaluate using said selected profile; and performing a discovery processoperation upon said at least one data source using said selectedprofile.
 18. The method of claim 17, further comprising modifying saidselected profile.
 19. The method of claim 17, further comprisingcalculating a charge for use of said selected profile.
 20. A computerprogram product comprising at least one non-transitory computer-readablestorage medium having computer-readable program code portions storedtherein and providing for associative relevancy knowledge profiling, thecomputer program product comprising: a first program code portionconfigured for providing at least two known reference sets; a secondprogram code portion configured for selecting at least two of said knownreference sets, including at least a first known reference set and asecond known reference set; a third program code portion configured forcreating at least one association between two of said known referencesets to generate at least part of an associative relevancy knowledgeprofile, wherein creating said at least one association comprises:receiving a user input, wherein the user input represents a user'sintention to create an association between said first known referenceset and said second known reference set and an association type for theassociation; and in response to receiving said user input, creating saidassociation with the association type, wherein said association is oneof said at least one association; and a fourth program code portionconfigured for storing said associative relevancy knowledge profile,wherein said associative relevancy knowledge profile is associatedwithin one or more containers each having at least one predefinedconstraint that biases at least one of said available known referencesets.